{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report3 - 服装分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FashionMNIST 是一个替代 MNIST 手写数字集的图像数据集。 它是由 Zalando（一家德国的时尚科技公司）旗下的研究部门提供。其涵盖了来自 10 种类别的共 7 万个不同商品的正面图片。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、报告目的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建神经网络，对运动鞋和衬衫等服装图像进行分类。利用网络爬虫抓取一些图片进行识别。但是本人深度神经网络方面的知识比较匮乏，就安装了tensorflow，利用tensorflow里的Keras神经网络库。对FashionMNIST里面的数据进行训练，得到一个精准的模型后再抓取网络图片进行识别。提升自己机器学习库的一个熟练应用。同时提高自己的爬取图片的能力，进一步提高编程能力与论文书写能力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、基础知识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 首先用到了tensorflow的Keras库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras是一个高级深度学习API，使用Python语言进行编写的。Keras能够在TensorFlow、Theano或CNTK上运行，这个意思就是Keras的后端引擎可以是这三者之一，用户可以进行显式的选择。重要的是Keras提供了一个简单和模块化的API来构建和训练我们需要的神经网络，比如卷积神经网络，循环神经网络等等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个Keras模型包含了4个基本元素：\n",
    "\n",
    "结构：指定了包含什么样的layers以及它们是怎么连接的；\n",
    "模型的状态：一组权重值weights\n",
    "优化器optimizer：通过compiling模型来定义\n",
    "一组losses和metrics：通过compiling模型或者调用add_loss()和add_metric()。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型构建过程包括三部分："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.设置层\n",
    "\n",
    "神经网络的基本组成部分是层。层会从向其馈送的数据中提取表示形式。希望这些表示形式有助于解决手头上的问题。\n",
    "\n",
    "大多数深度学习都包括将简单的层链接在一起。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.编译模型\n",
    "\n",
    "在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：\n",
    "\n",
    "损失函数 - 用于测量模型在训练期间的准确率。您会希望最小化此函数，以便将模型“引导”到正确的方向上。\n",
    "优化器 - 决定模型如何根据其看到的数据和自身的损失函数进行更新。\n",
    "指标 - 用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.训练模型 \n",
    "利用 model.fit 函数，将模型与训练数据进行“拟合”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 爬取图片时用到了requests库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "requests库功能过于强大，包括：保持活力和连接池、支持国际域名和网址、会话与Cookie持久性、浏览器式SSL验证、自动内容解码、基本/摘要式身份验证、自动解压缩、Unicode响应body、HTTP(s)代理支持、多部分文件上传、流媒体下载、连接超时、分块的请求、.netrc 支持等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主要有七种用法："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "requests.request()\t构造一个请求，支撑以下各方法的基础方法\n",
    "\n",
    "requests.get()\t   获取HTML网页的主要方法，对应HTTP协议的GET\n",
    "\n",
    "requests.head()\t   获取HTML网页头信息的方法，对应HTTP协议的HEAD\n",
    "\n",
    "requests.post()\t   向HTML网页提交POST请求的方法，对应HTTP协议的POST\n",
    "\n",
    "requests.put()\t   向HTML网页提交PUT请求的方法，对应HTTP协议的PUT\n",
    "\n",
    "requests.patch\t   向HTML网页提交局部修改，对应HTTP协议的PATCH\n",
    "\n",
    "requests.delete()\t向HTML网页提交删除请求的方法，对应HTTP协议的DELETE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、实验原理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主要包括四个步骤：\n",
    "### 1.数据加载\n",
    "### 2.数据预处理\n",
    "### 3.模型构建\n",
    "### 4.训练模型\n",
    "### 5.测试集测试\n",
    "### 6.使用模型去进行分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五、实验过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入所需库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.数据导入、分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下载资源包时遇到了很多问题，由于网络原因数据包一直无法下载最后询问同学得到从Keras数据库中可以下载，成果导入了数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape#训练分析数据的规模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels#查看标签为0—9的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape#测试数据为一千个，类型与训练数据相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_images[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.数据查看及预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将这些值缩小至 0 到 1 之间，然后将其馈送到神经网络模型。为此，请将这些值除以 255。请务必以相同的方式对训练集和测试集进行预处理：\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 检查训练集中的第一个图像，发现像素值处于 0 到 255 之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 显示训练集中的前 25 个图像，并在每个图像下方显示他们所属类别的标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.imshow(train_images[i],cmap=plt.cm.binary) \n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.xlabel(train_labels[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 发现标签不对，进过查询后才知道有十个标签与之对应"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat','Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.模型构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建神经网络需要先配置模型的层，然后再编译模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 该网络的第一层 tf.keras.layers.Flatten 将图像格式从二维数组（28 x 28 像素）转换成一维数组（28 x 28 = 784 像素）。将该层视为图像中未堆叠的像素行并将其排列起来。该层没有要学习的参数，它只会重新格式化数据。\n",
    "\n",
    "### 展平像素后，网络会包括两个 tf.keras.layers.Dense 层的序列。它们是密集连接或全连接神经层。第一个 Dense 层有 128 个节点（或神经元）。第二个（也是最后一个）层会返回一个长度为 10 的 logits 数组。每个节点都包含一个得分，用来表示当前图像属于 10 个类中的哪一类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(256, activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将训练数据输入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.4852 - accuracy: 0.8288\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.3660 - accuracy: 0.8671\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 2s 959us/step - loss: 0.3254 - accuracy: 0.8803\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 2s 921us/step - loss: 0.3025 - accuracy: 0.8879\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 2s 931us/step - loss: 0.2837 - accuracy: 0.8947\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 2s 913us/step - loss: 0.2710 - accuracy: 0.8987\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 2s 925us/step - loss: 0.2567 - accuracy: 0.9049\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 2s 922us/step - loss: 0.2470 - accuracy: 0.9070\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 940us/step - loss: 0.2380 - accuracy: 0.9111\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 2s 921us/step - loss: 0.2277 - accuracy: 0.9143\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x25b9cddb208>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)#对数据进行十次拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 准确率达到了 91.43%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.测试集测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.3480 - accuracy: 0.8845\n",
      "b\n",
      "Test accuracy: 0.8845000267028809\n"
     ]
    }
   ],
   "source": [
    "#将测试数据导入model.evaluate之中，得到测试精度\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('b\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试精度达到了88.45%，说明模型的识别准确度良好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.编写预测代码，应用模型进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将预测结果以图片的形式展示出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#正确的预测标签为蓝色，错误的预测标签为红色。数字表示预测标签的百分比（总计为 100）。\n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出测试集前25个图片的预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 发现预测整体比较准确对于难以辨认的鞋子还是会出错"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对于网站爬取图片进行识别"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 我从一个名为图行天下的网页爬取了20张裤子图片现在进行识别，爬取代码在另一个文件里<爬取图片.ipynb>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#处理爬取到的图片\n",
    "import os\n",
    "from PIL import Image\n",
    "import torchvision.transforms as transforms\n",
    "image_path='C://Users/LENOVO/clothes_img'#爬取图片的路径\n",
    "image_list=os.listdir(image_path)#获取图片的名字\n",
    "plt.figure(figsize=(10,10))\n",
    "shuju=[]\n",
    "for i in range(20):\n",
    "    image=Image.open(image_path+'\\\\'+image_list[i])#打开图片   \n",
    "    img=image.resize((28,28))#将图片改为28*28像素的\n",
    "    image_transforms=transforms.Compose([transforms.Grayscale(1)])\n",
    "    img=image_transforms(img)#此处为借鉴任家辉同学的，我真不会了 \n",
    "    img=np.asarray(img)\n",
    "    img=255-img#将图片反色处理\n",
    "    shuju.append(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuju=np.array(shuju)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 显示处理好的图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(20):\n",
    "    plt.subplot(4,5,i+1)\n",
    "    plt.imshow(shuju[i],cmap=plt.cm.binary) \n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进行识别预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#利用模型得到标签\n",
    "predictions = probability_model.predict(shuju)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将图片与标签进行匹配\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(20):\n",
    "    predicted_label = np.argmax(predictions[i])\n",
    "    plt.subplot(4,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(shuju[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[predicted_label])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 经过一个夜晚的奋斗得到的竟是全错的结果我破防了，说明数据源无法匹配模型得到的所有数据都是有问题的。所以要想跟好的匹配得使用相似图片进行训练。也有可能是图片处理出了问题，但是我能力不足，后面有时间再向大能请教。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以后有时间的话，我还会继续完善这个代码，我对此还是很有兴趣的。鉴于目前考试压力大就暂且这样吧"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.虽然学习的过程是痛并快乐着的，但是结束后去反思自己得到的东西也是很多的。\n",
    "首先我明白了有些事要量力而行，不是你努力就会有结果的。人生遇到的挑战有很多，最可悲的是哪些逃避挑战的人。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.最重要的是我掌握了python的环境配置，和利用镜像源来加快下载速度。\n",
    "在安装tensorflow的过程中出现了很多问题，anaconda我来来回回卸载安装了无数次。刚开始不知道为啥，无论用navigator还是prompt在新建的环境中都无法下载tensorflow安装包，\n",
    "有一次下好后整个python环境都崩了，我苦恼了好多天。最终重新下载了新版anaconda，利用镜像下载成功。\n",
    "同时利用navigator在新环境中安装了jupyter才能正常的在这里编程\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.基本掌握了Keras进行神经网络搭建的基本方法，了解了Keras的一些重要指令。\n",
    "正是因为Python中可以应用许多机器学习的库，才使python能成为机器学习的主阵地，所以掌握一些库的用法是十分重要的。\n",
    "虽然对与程序员来说了解一些算法的本质与底层搭建十分重要，但是我个人以后主要是利用Python进行一些实际问题的解决所以会用就行。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.学会了简单的图片爬取\n",
    "这个能力是十分有用的，要是以后看到了某些网页上的图片无法下载我可以直接利用爬取功能来获得。\n",
    "当然图片爬取只是很小的功能，以后我还会学习更多的爬虫知识。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.关于jupyter的应用与操作能力又有很大的提升。\n",
    "以前喜欢用spider，但是当你一旦入门jupyter就再也不想用spider了，因为这个编辑界面及简洁明了又十分好用。尤其是对于爬虫还有注释行的代码编写简直太好用了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "鸣谢：布老师、李老师、以及任家辉同学"
   ]
  }
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